import sys
import os
import logging
import re
import json
from typing import Dict, List, Any, Optional, Tuple

# 添加项目根路径到 sys.path
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..'))

# 导入 MySQLUtil
from shared.utils.MySQLUtil import MySQLUtil

# 设置日志
logger = logging.getLogger(__name__)

def get_职业期待吻合情况分布(
    project_id: int,
    questionnaire_ids: List[int],
    product_code: Optional[str] = None,
    project_code: Optional[str] = None,
    region_code: Optional[str] = None,
    education: Optional[str] = None
) -> Dict[str, Any]:
    """
    职业期待吻合情况分布 - 指标计算函数
    
    ## 指标说明
    计算学生职业期待与实际工作吻合程度的分布情况，通过分析问卷中"工作与求职意向吻合程度"问题的回答情况，
    计算各选项的选择人数占比，并汇总前三项(很吻合、吻合、基本吻合)的占比作为吻合度指标。
    
    ## Args
        project_id (int): 项目ID，用于查询项目配置信息
        questionnaire_ids (List[int]): 问卷ID集合，用于确定数据范围
        product_code (Optional[str]): 产品编码，用于路由到特定计算逻辑
        project_code (Optional[str]): 项目编码，用于路由到特定计算逻辑
        region_code (Optional[str]): 区域编码，用于路由到特定计算逻辑
        education (Optional[str]): 学历筛选条件，可选值：[本科毕业生，专科毕业生，硕士研究生，博士研究生]
        
    ## 示例
    ### 输入
    ```json
    {
        "project_id": 5895,
        "questionnaire_ids": [11158, 11159],
        "education": "博士研究生"
    }
    ```
    
    ### 输出
    ```json
    {
        "success": true,
        "message": "ok", 
        "code": 0,
        "result": {
            "overall_ratio": 0.8,
            "options": [
                {"key": "1", "val": "很吻合", "count": 125, "ratio": 0.25},
                {"key": "2", "val": "吻合", "count": 175, "ratio": 0.35},
                {"key": "3", "val": "基本吻合", "count": 100, "ratio": 0.2},
                {"key": "4", "val": "不吻合", "count": 50, "ratio": 0.1},
                {"key": "5", "val": "很不吻合", "count": 50, "ratio": 0.1}
            ]
        }
    }
    ```
    """
    logger.info(f"开始计算指标: 职业期待吻合情况分布, 项目ID: {project_id}")
    
    try:
        db = MySQLUtil()  

        # 1. 查询项目配置信息
        project_sql = """
        SELECT client_code, item_year, dy_target_items, split_tb_paper 
        FROM client_item 
        WHERE id = %s
        """
        project_info = db.fetchone(project_sql, (project_id,))
        if not project_info:
            raise ValueError(f"未找到项目ID={project_id}的配置信息")

        client_code = project_info['client_code']
        item_year = project_info['item_year']
        split_tb_paper = project_info['split_tb_paper']
        
        logger.info(f"项目配置: client_code={client_code}, item_year={item_year}, split_tb_paper={split_tb_paper}")

        # 2. 计算 shard_tb_key
        shard_tb_key = re.sub(r'^[A-Za-z]*0*', '', client_code)
        logger.info(f"计算得到 shard_tb_key: {shard_tb_key}")

        # 3. 查询问卷信息
        questionnaire_sql = f"""
        SELECT id, dy_target 
        FROM wt_template_customer 
        WHERE id IN ({','.join(['%s'] * len(questionnaire_ids))})
        """
        questionnaires = db.fetchall(questionnaire_sql, tuple(questionnaire_ids))
        if not questionnaires:
            raise ValueError(f"未找到问卷ID集合={questionnaire_ids}的配置信息")
        
        logger.info(f"查询到问卷信息: {questionnaires}")

        # 4. 过滤GRADUATE_SHORT调研对象的问卷
        valid_questionnaire_ids = [q['id'] for q in questionnaires if q['dy_target'] == 'GRADUATE_SHORT']
        if not valid_questionnaire_ids:
            raise ValueError("未找到目标调研对象的问卷ID")
            
        logger.info(f"找到有效问卷ID: {valid_questionnaire_ids}")

        # 5. 查询问题信息
        question_sql = """
        SELECT id, wt_code, wt_obj 
        FROM wt_template_question_customer 
        WHERE cd_template_id = %s AND wt_code = 'T00000381' AND is_del = 0
        """
        question_info = db.fetchone(question_sql, (valid_questionnaire_ids[0],))
        if not question_info:
            raise ValueError("未找到指定问题编码(T00000381)的问题信息")
            
        logger.info(f"找到问题信息: {question_info['id']}")

        # 6. 解析问题选项
        wt_obj = json.loads(question_info['wt_obj'])
        options = []
        for item in wt_obj['itemList']:
            options.append({
                'key': item['key'],
                'val': item['val'],
                'weight': item.get('weight', 1)
            })

        # 7. 构建动态表名
        answer_table = f"re_dy_paper_answer_{split_tb_paper}"
        student_table = f"dim_client_target_baseinfo_student_{item_year}"

        # 8. 构建SQL查询条件
        education_condition = f"AND s.education = '{education}'" if education else ""
        
        # 9. 查询答案数据
        sql = f"""
        SELECT
            SUM(t1.c1) as c1,
            SUM(t1.c2) as c2,
            SUM(t1.c3) as c3,
            SUM(t1.c4) as c4,
            SUM(t1.c5) as c5
        FROM {answer_table} t1
        JOIN {student_table} s ON t1.target_no = s.target_no
        WHERE
            t1.cd_template_id = %s
            AND t1.wid = %s
            AND t1.ans_true = 1
            AND s.shard_tb_key = %s
            AND s.item_year = %s
            {education_condition}
        """
        result = db.fetchone(sql, (valid_questionnaire_ids[0], question_info['id'], shard_tb_key, item_year))
        if not result:
            raise ValueError("未找到有效的答案数据")
        
        # 10. 计算各选项占比和详细选项数据
        total = sum([float(result[f'c{i}'] or 0) for i in range(1, 6)])
        if total == 0:
            overall_ratio = 0
            options = [
                {"key": "1", "val": "很吻合", "count": 0, "ratio": 0},
                {"key": "2", "val": "吻合", "count": 0, "ratio": 0},
                {"key": "3", "val": "基本吻合", "count": 0, "ratio": 0},
                {"key": "4", "val": "不吻合", "count": 0, "ratio": 0},
                {"key": "5", "val": "很不吻合", "count": 0, "ratio": 0}
            ]
        else:
            overall_ratio = float((result['c1'] or 0) + (result['c2'] or 0) + (result['c3'] or 0)) / total
            options = [
                {"key": "1", "val": "很吻合", "count": result['c1'] or 0, "ratio": float(result['c1'] or 0) / total},
                {"key": "2", "val": "吻合", "count": result['c2'] or 0, "ratio": float(result['c2'] or 0) / total},
                {"key": "3", "val": "基本吻合", "count": result['c3'] or 0, "ratio": float(result['c3'] or 0) / total},
                {"key": "4", "val": "不吻合", "count": result['c4'] or 0, "ratio": float(result['c4'] or 0) / total},
                {"key": "5", "val": "很不吻合", "count": result['c5'] or 0, "ratio": float(result['c5'] or 0) / total}
            ]

        logger.info(f"指标 '职业期待吻合情况分布' 计算成功")
        return {
            "success": True,
            "message": "ok",
            "code": 0,
            "result": {
                "overall_ratio": overall_ratio,  # 职业期待吻合度：前三项（很吻合、吻合、基本吻合）占前五项总和的百分比
                "options": options  # 各选项的详细统计信息
            }
        }

    except Exception as e:
        logger.error(f"计算指标 '职业期待吻合情况分布' 时发生错误: {str(e)}", exc_info=True)
        return {
            "success": False,
            "message": f"数据获取失败: 职业期待吻合情况分布",
            "code": 500,
            "error": str(e)
        }